Assessing Neuromuscular Fatigue

Striking a healthy balance between training and recovery is integral to a successful programme. Understanding the fatigue responses to training and/or competition can play an important role in avoiding unplanned decrements in performance and reduce the likelihood of illnesses and injuries associated with ‘non-functional overreaching’ (typically known as overtraining).

How can we assess fatigue?

There are a number of ways we can attempt to quantify and monitor fatigue. Perhaps the easiest methods to employ are subjective assessments such self-reported questionnaires. For this reason these types of assessment tend to be the most commonly used tools by high-performance coaches (Taylor et al. 2012).

What’s the disadvantage of questionnaires?

Whilst self-reported measures have been shown to be sensitive to changes in training load (Kellmann & Günther 2000; Halson et al. 2003; Coutts et al. 2007), the subjective nature of such measures should be considered. It is possible that athletes may over- or under-report in these questionnaires, often termed ‘faking bad’ or ‘faking good’, to achieved a desired outcome (Meeusen et al. 2013).

Why measure neuromuscular function?

Measures of neuromuscular (NM) function are often used to assess recovery because of their greater utility to monitor relatively low-level NM fatigue compared with other indirect markers such as subjective questionnaires (Twist & Highton 2013). These measures provide an objective measure for the coach or athlete to interpret which, for good or for bad, do not consider the perceptions of the athlete.

Why use jump based measures?

The most common tool used to assess NM fatigue is a countermovement jump (CMJ) (Taylor et al. 2012). However, typical performance variables such a jump height or peak power may always not be sensitive enough to detect changes in NM function.

What measures are the most sensitive?

Considering an array of different CMJ variables, Cormack et al. (2008) reported that ratio of flight time to contraction time (FT:CT) during a countermovement jump provided the best indication of NM fatigue following an Australian football match. Whilst FT (used to measure jump height) was relatively unaffected (<1% decrease) FT:CT was reduced by an average of 7% at 24 hours post-match. As such, decreases in FT:CT appear largely dependent on an increase in the duration of contraction.

When fatigue goes up, speed comes down

Similar results have been reported by in collegiate team-sport athletes Gathercole et al. (2015). They observed that CMJ performance was diminished immediately post-exercise (a fatiguing Yo-Yo test) but had recovered at 72 hours. Importantly, alterations in jump strategy (i.e. decreased eccentric utilisation and increased jump duration) persisted at 72 hours. Also, another investigation conducted in elite snowboard cross athletes by the same research group (Gathercole et al. 2015) reported that jump performance was not impaired 30 minutes following fatigue (stair climbs to fatigue) but the eccentric utilisation and jump duration were impaired.

The SSC appears to be the key

Taken together, these results of these investigations suggest that practitioners need to examine variables that reflect stretch shortening cycle (SSC) capacity if seeking to use jump testing as an assessment of NM fatigue. Athletes may be able to maintain performance (i.e. jump height) by using a different (and slower) movement strategy.

Why is the SSC important?

SSC activities have been proposed in the study of NM fatigue by Komi (2000) as metabolic, mechanical, and neural elements are all challenged during this type of activity. As such, a fatigue-related disturbance in any of the elements could impair SSC performance and may explain why such tests are sensitive to low-level changes in NM function.

Does jump monitoring work long term?

Due to constraints of both time and budget, investigations in most fields of sports science tend to be conducted over relatively short time periods (rarely longer than 6-8 weeks). There are a few studies out there though. For example, Balsalobre-Fernández et al. (2014) reported that CMJ height correlated well with cortisol concentration (i.e. the stress response) in elite endurance runners over a 39 week season. Perhaps most importantly, they observed significant differences in jump performance (~8-9%) when tested before athletes’ best and worst performances of the year.

What about the FT:CT?

Cormack et al. (2008) were the first to evaluate the potential of using this technique over an entire season. They reported a clear pattern of impaired FT:CT following AFL match-play and that this also appeared to mirror changes in cortisol concentration. Mooney et al. (2013) have since shown that decreases in FT:CT are linked to impaired high-intensity running capacity in the same population.

What about drop jumps then?

Hopefully this has given you a little introduction into the potential of jump based monitoring. Today we’ve focused on the potential power of the bog-standard CMJ to monitor NM fatigue, next time out we’ll take a look whether the drop jump may prove an even more powerful assessment tool.